Smarter Data Science: Succeeding with Enterprise-Grade Data and AI Projects

Cover
Praise For This Book
Title Page
Copyright
About the Authors
Acknowledgments
Contents at a Glance
Contents
Foreword for Smarter Data Science
Epigraph
Preamble
Chapter 1 Climbing the AI Ladder
	Readying Data for AI
	Technology Focus Areas
	Taking the Ladder Rung by Rung
	Constantly Adapt to Retain Organizational Relevance
	Data-Based Reasoning Is Part and Parcel in the Modern Business
	Toward the AI-Centric Organization
	Summary
Chapter 2 Framing Part I: Considerations for Organizations Using AI
	Data-Driven Decision-Making
		Using Interrogatives to Gain Insight
		The Trust Matrix
		The Importance of Metrics and Human Insight
	Democratizing Data and Data Science
	Aye, a Prerequisite: Organizing Data Must Be a Forethought
		Preventing Design Pitfalls
	Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time
	Quae Quaestio (Question Everything)
	Summary
Chapter 3 Framing Part II: Considerations for Working with Data and AI
	Personalizing the Data Experience for Every User
	Context Counts: Choosing the Right Way to Display Data
	Ethnography: Improving Understanding Through Specialized Data
	Data Governance and Data Quality
		The Value of Decomposing Data
		Providing Structure Through Data Governance
		Curating Data for Training
		Additional Considerations for Creating Value
	Ontologies: A Means for Encapsulating Knowledge
	Fairness, Trust, and Transparency in AI Outcomes
	Accessible, Accurate, Curated, and Organized
	Summary
Chapter 4 A Look Back on Analytics: More Than One Hammer
	Been Here Before: Reviewing the Enterprise Data Warehouse
	Drawbacks of the Traditional Data Warehouse
	Paradigm Shift
	Modern Analytical Environments: The Data Lake
		By Contrast
		Indigenous Data
		Attributes of Difference
	Elements of the Data Lake
	The New Normal: Big Data Is Now Normal Data
		Liberation from the Rigidity of a Single Data Model
		Streaming Data
		Suitable Tools for the Task
		Easier Accessibility
		Reducing Costs
		Scalability
		Data Management and Data Governance for AI
	Schema-on-Read vs. Schema-on-Write
	Summary
Chapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail
	A Need for Organization
		The Staging Zone
		The Raw Zone
		The Discovery and Exploration Zone
		The Aligned Zone
		The Harmonized Zone
		The Curated Zone
	Data Topologies
		Zone Map
		Data Pipelines
		Data Topography
	Expanding, Adding, Moving, and Removing Zones
	Enabling the Zones
		Ingestion
		Data Governance
		Data Storage and Retention
		Data Processing
		Data Access
		Management and Monitoring
		Metadata
	Summary
Chapter 6 Addressing Operational Disciplines on the AI Ladder
	A Passage of Time
	Create
		Stability
		Barriers
		Complexity
	Execute
		Ingestion
		Visibility
		Compliance
	Operate
		Quality
		Reliance
		Reusability
	The xOps Trifecta: DevOps/MLOps, DataOps, and AIOps
		DevOps/MLOps
		DataOps
		AIOps
	Summary
Chapter 7 Maximizing the Use of Your Data: Being Value Driven
	Toward a Value Chain
		Chaining Through Correlation
		Enabling Action
		Expanding the Means to Act
	Curation
	Data Governance
	Integrated Data Management
		Onboarding
		Organizing
		Cataloging
		Metadata
		Preparing
		Provisioning
		Multi-Tenancy
	Summary
Chapter 8 Valuing Data with Statistical Analysis and Enabling Meaningful Access
	Deriving Value: Managing Data as an Asset
		An Inexact Science
	Accessibility to Data: Not All Users Are Equal
	Providing Self-Service to Data
	Access: The Importance of Adding Controls
	Ranking Datasets Using a Bottom-Up Approach for Data Governance
	How Various Industries Use Data and AI
	Benefiting from Statistics
	Summary
Chapter 9 Constructing for the Long-Term
	The Need to Change Habits: Avoiding Hard-Coding
		Overloading
		Locked In
		Ownership and Decomposition
		Design to Avoid Change
	Extending the Value of Data Through AI
	Polyglot Persistence
	Benefiting from Data Literacy
		Understanding a Topic
		Skillsets
		It’s All Metadata
		The Right Data, in the Right Context, with the Right Interface
	Summary
Chapter 10 A Journey’s End: An IA for AI
	Development Efforts for AI
	Essential Elements: Cloud-Based Computing, Data, and Analytics
		Intersections: Compute Capacity and Storage Capacity
		Analytic Intensity
		Interoperability Across the Elements
		Data Pipeline Flight Paths: Preflight, Inflight, Postflight
		Data Management for the Data Puddle, Data Pond, and Data Lake
	Driving Action: Context, Content, and Decision-Makers
	Keep It Simple
	The Silo Is Dead; Long Live the Silo
	Taxonomy: Organizing Data Zones
	Capabilities for an Open Platform
	Summary
Glossary of Terms
Index
EULA